summary.clogitL1: Summary after fitting conditional logistic regression with...

In clogitL1: Fitting Exact Conditional Logistic Regression with Lasso and Elastic Net Penalties

Summary after fitting conditional logistic regression with elastic net penalties

Description

Takes a clogitL1 object and produces a summary of the sequence of models fitted.

Usage

 ## S3 method for class 'clogitL1'
 summary(object, ...)

Arguments

object	an object of type clogitL1.
...	any additional arguments passed to summary method

Details

Returns a list with a elements Coefficients, which holds the matrix of coefficients estimated (each row holding the estimates for a given value of the smoothing parameter) and Lambda, which holds the vector of smoothing parameters at which fits were produced.

References

https://www.jstatsoft.org/v58/i12/

See Also

clogitL1

Examples

set.seed(145)

# data parameters
K = 10 # number of strata
n = 5 # number in strata
m = 2 # cases per stratum
p = 20 # predictors

# generate data
y = rep(c(rep(1, m), rep(0, n-m)), K)
X = matrix (rnorm(K*n*p, 0, 1), ncol = p) # pure noise
strata = sort(rep(1:K, n))

par(mfrow = c(1,2))
# fit the conditional logistic model
clObj = clogitL1(y=y, x=X, strata)
summary(clObj)

clogitL1 documentation built on Sept. 18, 2026, 9:13 a.m.